class_docstr: Plot ROC curve
  
  Parameters
  ----------
  fpr : ndarray of shape (>2,), list of lists or list of numbers
      Increasing false positive rates such that element i is the false
      positive rate of predictions with score >= `thresholds[i]`.
  
  tpr : ndarray of shape (>2,), list of lists or list of numbers
      Increasing true positive rates such that element `i` is the true
      positive rate of predictions with score >= `thresholds[i]`.
  
  label : list of str, default: None
      Set curve labels
  
  ax: matplotlib Axes, default: None
      Axes object to draw the plot onto, otherwise uses current Axes
  
  Examples
  --------
  .. plot:: ../examples/roc_binary_classification.py
  
  .. plot:: ../examples/roc_comparison.py
  
  .. plot:: ../examples/roc_multi_classification.py
  
  Notes
  -----
  .. versionadded:: 0.8.4
code: def _get_data(self):
      fpr = self.fpr
      tpr = self.tpr
  
      # for serialization
      for i in range(len(fpr)):
          if isinstance(fpr[i], np.ndarray):
              fpr[i] = fpr[i].tolist()
              tpr[i] = tpr[i].tolist()
  
      return {
          "class": "sklearn_evaluation.plot.ROC",
          "version": __version__,
          "fpr": fpr,
          "tpr": tpr,
      }
  
imports: import numpy
  import matplotlib.pyplot
  from sklearn.metrics import roc_curve, auc
  from sklearn.preprocessing import label_binarize
  from ..telemetry import SKLearnEvaluationLogger
  from ..util import is_column_vector, is_row_vector
  from sklearn_evaluation import __version__
  import json
  from pathlib import Path
  from sklearn_evaluation.plot.plot import AbstractPlot, AbstractComposedPlot
  from ploomber_core.exceptions import modify_exceptions
label_desc: Use image node_id 4 for calling the ROC obj's underlying member method code with example usage: obj._get_data() and returns: dict